Yeonsik Jo

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 190 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #26,711 of 35,345 by trust here, #28,481 by citations
• Computer Vision and Pattern Recognition — 44 citations into it; #16,361 of 21,575 by trust here, #18,266 by citations
• Representation & Self-Supervised Learning — 20 citations into it
• Meta & Continual Learning — 15 citations into it; #835 of 1,041 by trust here, #963 by citations
• Optimization & Training — 11 citations into it; #2,128 of 2,601 by trust here, #2,313 by citations
• Model Architectures — 10 citations into it
• Reinforcement Learning — 8 citations into it
• Efficiency & Compression — 7 citations into it

Whom Yeonsik Jo trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Ilya Sutskever
6. Diederik P. Kingma
7. Yann LeCun
8. Max Welling
9. Marc'Aurelio Ranzato
10. Alex Graves

Who trusts Yeonsik Jo overall

1. Eden Belouadah
2. Adrian Daniel Popescu

Papers from Yeonsik Jo's perspective

Ordered by how strongly the researchers Yeonsik Jo trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Confidence Calibration for Incremental Learning (2020) — 3 citations
• Incremental Learning with Maximum Entropy Regularization: Rethinking Forgetting and Intransigence (2019) — 23 citations